Ranking of industrial forest plantations in terms of sustainability: A multicriteria approach

J Environ Manage. 2016 Sep 15:180:123-32. doi: 10.1016/j.jenvman.2016.05.022. Epub 2016 May 20.

Abstract

As forest managers and owners must have precise assessments of sustainability, in this study we have proposed a methodology based on multi-criteria techniques for assessing sustainability in industrial forest plantations and establishing a ranking of these plantations in terms of sustainability. First, we identified and have briefly described a set of sustainability indicators (economic, environmental and social). Next, we developed a statistical procedure to determine if a linear relationship existed between the indicators. With this analysis, the final set of indicators was defined and normalized. Then, we formulated four goal programming models, by which to aggregate the different indicators. In these models, we introduced the preferences of the decision makers for each indicator, using a survey with questions formulated in a pairwise comparison format. The procedure was applied to 30 Eucalyptus globulus Labill. plantations in northwestern Spain and 11 indicators were selected in order to define the sustainability. The results showed several rankings under each goal programming model. Although the results may not be the same in the different models, some plantations are always the most sustainable, while others are always the worst in terms of sustainability. The combination of initial values of indicators, goal programming models and preferences of stakeholders (preferential weights and targets) influence the results, and it cannot be predicted a priori which plantation is the best/worst in terms of sustainability. In our case study, we show how changes in preferential weights and targets substantially modify the results obtained.

Keywords: Eucalyptus plantations; Forest management; Goal programming; Indicators; Multiple criteria decision making; Sustainability.

MeSH terms

  • Conservation of Natural Resources / methods*
  • Decision Support Techniques*
  • Forests*
  • Humans
  • Industry
  • Models, Theoretical
  • Spain